Sometimes it’s very uncertain and you don’t even realize when you run into data migration problems. However, being aware of the common hurdles that could potentially derail your project will increase the likelihood of achieving a smooth transition of data to your new system. The most common data migration problems that are often not addressed and the factors that far too often lead to failure are as follows:
1. Poor Knowledge of Source Data
Not being aware of the problems that exist in your data is the reason for the poor knowledge of source data. It can be all too easy to get complacent and assume that your data can easily be configured into the parameters of a new system however the reality could mean critical failures when it comes to user acceptance. So to ensure success, you need a good understanding of the source data.
2. Underestimating Data Analysis
Constraints in a computer system can be an issue as information can be hidden in obscure places because often there are not specific fields to hold all elements of the data and sometimes the users may also be not aware of the purpose of the available fields. This result is outdated data getting transferred during migration which sometimes is discovered after the project is completed. Due to this, there is not enough time available to identify and correct this data. Performing a thorough data analysis at the earliest stage usually while designing and planning can uncover these hidden errors.
3. Lack of Integrated Processes
Data migration is Disparate technologies used by a set of people. Using spreadsheets to document data specifications which are not easy to translate while performing data transformations and which are prone to human errors is a classic example. This usually leads to failure in transferring data and problems in the development, testing and implementation stages. Organisations must look to utilise a platform that successfully links the critical inputs and outputs from each of the stages to help reduce error and save time and money.
4. Inability to Validate a Specification
Critical misses of the early stage of data can have repercussions later in the chain of activities may well have an understanding of your source data, but is not necessary that it would result in a strong specification for migrating and modifying data into a target system. Validating your data transformation specifications early on with actual data, rather than just documented aspirations can increase the confidence in executing the rest of the steps.
5. Failure to Validate the Implementation
You can hit a brick wall because of lack of test cases even where your knowledge of source data is evident. To avoid the risk of developing problems you need to explore various scenarios before it is too late. Using full volume data from the real world helps to cover a wider range of possibilities while testing your migration.
6. Late Evaluation of the Final Results.
The problem of late evaluation usually occurs in the testing stage, where the user only gets to see the actual data once it is loaded into the new system at the end of the development. Due to the incompatibility of the data in the new system takes place. Time, money and the embarrassment of a delayed project can be avoided by introducing early and agile testing phases and getting your users involved in evolving the test cases as they see actual prototypes of the data output.
7. Lack of Collaboration
Data migrations along with disparate people also involve internal employees and external contractors in some cases. Some of these people may not even be in the same location. Working in silos can reduce efficiency, create more data silos and sometimes lead to misinterpretations. Collaborative tools enable all parties invested in a migration to see the same picture of data as it moves through the project stages, leaving little room for assumptions and misunderstandings.
